Machine Learning

Understanding the Importance of Machine Learning for Health care

There has been an exponential advancement in technology in recent times with companies breaking new grounds in the area of artificial intelligence, chatbots, 3D technology and simulations and Machine learning. Machine learning for health care is using this great advancement in technology to make the health care industry more efficient. Many industries besides the Healthcare Industry,  use machine learning for more efficiency. Some of these industries are:

  • Marketing and Sales
  • Financial Services
  • Government
  • Transportation
  • Oil and Gas

These industries work with big data just like the health care industry and have found ways to use the benefits of Machine learning to their advantage. The Healthcare industry is doing the same. It’s very important to use Machine Learning for health care in order to improve the quality of life of patients and the efficiency of people in the health industry.

Machine Learning

Before listing the benefits of Machine Learning for Health Care, we should first understand what it means. Put simply, Machine learning is a method of data analysis that automates the development and building of analytical models. With machine learning the algorithms and programs iteratively and independently learn from data. Hence, computers can find insights and patterns that would be missed by the human eye without being programmed to do so.

Benefits of Machine Learning for Health Care

It helps to reduce readmissions in Hospitals:

Machine Learning can predict which patients are more likely to be readmitted for a similar or related illness or which patients have displayed this pattern in this past. With this information, hospitals can take measures to prevent or reduce these readmissions.

It prevents hospital-acquired infections (HAIs):

Central-line associated bloodstream infections (CLABSIs) are known to be very serious issues that affect patients. When germs and/or bacteria enter the bloodstream through the central line it puts patients at great risk. Machine Learning can predict which patients are more susceptible to CLABSIs giving physicians the chance to be proactive and take extra measure to prevent it.

It reduces hospital Length-of-Stay (LOS):

With the information gotten from Machine Learning, Hospitals can reduce the length of stay of patients. For example, as mentioned above, preventing hospital-acquired infections like Central-line associated bloodstream infections means a higher turnover for patient beds. This is a great benefit to both the patients and the hospital.

It predicts chronic diseases:

Machine LearningMachine learning can predict the likelihood of a patient to develop a chronic disease. It can also help diagnose unknown or misdiagnosed chronic diseases and infections. In cases where these diseases are infectious and contagious, this information helps prevent it’s spread and gives the hospital or health facility a head start in dealing with outbreaks.

It reduces 1-year mortality:

Death within one year of discharge is a problem hospital and patients have to deal with. With the predictive data gotten from machine learning, hospitals can predict which patients are more susceptible to this and thus provide the appropriate care and support system for the patients after they are discharged.

With the hospitals following up on these patients it reduces or even completely prevents the likelihood of a 1-year mortality. Also, with machine learning pointing out which patients need this care, the hospital doesn’t have to spread its resources thin by providing this follow-up to those who don’t need it. This improves efficiency and patient satisfaction and most importantly, survival rate.

It predicts patient’s propensity-to-pay:

Using patient information and data they provide, Machine Learning can predict which patients are more likely to have difficulty paying their health bills. That way, patients can be offered financial assistance or payment plans to help them pay their bills. This way the hospital doesn’t lose money and the patients are not in debt. This, of course, should not be used as a way to deny patients treatment as that would be unethical. The issue of healthcare is quite complicated and very expensive with dysfunctional health insurance programs. Healthcare should be free for all but sadly it’s not and as patients try to pay their medical bills and figure out the health insurance landscape, hospitals should do their best to distribute care evenly and not deprive anyone of care.

All these benefits make the practice of medicine more proactive than reactive. Hospitals and Health Organizations that use Machine learning to reap these benefits and stay ahead of the game in terms of efficiency and quality of care to patients.